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Research papers on AI in medical diagnosis

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  1. A survey on deep learning in medical image analysis

    Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, et al. · 2017 · Medical Image Analysis · 15,208 citations

    This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellula

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  2. A survey on Image Data Augmentation for Deep Learning

    Connor Shorten, Taghi M. Khoshgoftaar · 2019 · Journal Of Big Data · 13,088 citations

    Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augme

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  4. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

    Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, et al. · 2021 · Journal Of Big Data · 7,896 citations

    In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natur

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  5. Artificial intelligence in healthcare: past, present and future

    Fei Jiang, Yong Jiang, Hui Zhi, et al. · 2017 · Stroke and Vascular Neurology · 4,866 citations

    Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. W

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  6. The potential for artificial intelligence in healthcare

    Thomas H. Davenport, Ravi Kalakota · 2019 · Future Healthcare Journal · 3,834 citations

    The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

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  7. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

    Erico Tjoa, Cuntai Guan · 2020 · IEEE Transactions on Neural Networks and Learning Systems · 2,403 citations

    Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still

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  8. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

    Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs, et al. · 2022 · Medical Image Analysis · 1,298 citations

    With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

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  9. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

    Myura Nagendran, Yang Chen, Christopher A. Lovejoy, et al. · 2020 · BMJ · 1,148 citations

    OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. DESIGN: Systematic review. DATA SOURCES: Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning r

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  10. RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

    Yogesh Kumar, Apeksha Koul, Ruchi Singla, et al. · 2022 · Journal of Ambient Intelligence and Humanized Computing · 1,045 citations

    Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive su

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  11. Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification

    Rawan Ghnemat, Sawsan Alodibat, Qasem Abu Al-Haija · 2023 · Journal of Imaging · 60 citations

    Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneum

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  13. Artificial Intelligence in Medical Imaging: Applications of Deep Learning for Disease Detection and Diagnosis

    Abhinav Deshmukh · 2024 · Universal Research Reports · 7 citations

    The integration of artificial intelligence (AI) and deep learning techniques into medical imaging has revolutionized disease detection and diagnosis. This paper provides a comprehensive overview of the applications of deep learning in medical imaging and its impact on healthcare. The paper begins with an introduction to the fundamentals of deep learning, emphasizing convolutional neural networks (CNNs) and their relevance in analyzing medical images. It then explores various applications of deep learning in medical imaging, including automated disease detection and classification, image segmentation for precise anatomical localization, quantitative analysis for predictive modeling, personali

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  18. Deep Learning-Based AI Framework for Automated Medical Diagnosis

    Vishal Khanna · 2026 · i-manager's Journal on Artificial Intelligence & Machine Learning

    The rapid adoption of artificial intelligence (AI) in healthcare has opened new avenues for automated medical diagnosis, offering the potential to significantly improve clinical decision-making, diagnostic accuracy, and early disease detection. With the increasing availability of digital health records, medical imaging, and laboratory data, AI-driven systems are being explored as effective tools to support clinicians in managing complex and large-scale medical information. Traditional diagnostic processes often rely on manual interpretation and expert judgment, which can be time-consuming, subject to human error, and limited by inter-observer variability. In this context, deep learning techn

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  19. Deep Learning for Medical Imaging Analysis

    Gayatri Gupta, Aafila Shrivastava · 2026 · Journal of Artificial Intelligence and Information

    This paper presents a comprehensive analysis of deep learning applications in medical imaging analysis. We examine the evolution of medical image processing from traditional computer vision approaches to sophisticated deep learning solutions, highlighting the critical role of artificial intelligence in modern healthcare diagnostics. Our research encompasses multiple dimensions of medical imaging analysis, including disease detection, segmentation, classification, and automated diagnosis across various imaging modalities. Through extensive evaluation across diverse medical imaging datasets, we demonstrate significant improvements in diagnostic accuracy and efficiency. Our findings show that d

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  21. Artificial Intelligence in Oncologic Imaging: Deep Learning, Radiomics, and Clinical Integration for Precision Cancer Diagnosis

    Ohmini Krishnamurthy Rajendran · 2026 · International Journal of Drug Delivery Technology

    Radiological oncology is using AI to analyse complicated images, analyse tumours, and make treatment decisions. This study focuses on deep learning, radiomics, and radiogenomics frameworks for AI applications in breast, lung, prostate, brain, gastrointestinal, and metastatic cancers. Outperforming expert radiologists, these tools have improved lesion identification, segmentation, risk prediction, and molecular phenotype inference. In addition, the review discusses how AI influences workflow triage, report standardization, and radiologist efficiency in digital mammography, MRI, PET/CT, and CT. Despite recent advancements, the need for AI outputs that can be explained in therapeutic contexts,

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